A GAN model that is trained on large-scale natural images captures rich texture and shape priors.
Previous studies [1]}, [2]}, [3]}, [4]} have shown that such priors can be harvested through GAN inversion to benefit various image restoration tasks. Nonetheless, it remains underexplored how to exploit the priors without the expensive optimization during inversion.
